17 results about "Sensor model" patented technology

Model selection is disclosed. Features used as inputs to models are scored in terms of importance and health. The importance and health scores are combined in order to generate a model score for each model. The model with a score above a threshold score is selected and deployed.

The application discloses a kind of scanning path planning methods, equipment, medium and product for aircraft upper surface, which comprises: based on curvature, normal vector and point cloud density joint detection algorithm, the overall point cloud data of the aircraft to be scanned is divided into data, to obtain each target point cloud partition corresponding to each target functional partition in the upper surface of the aircraft, after rectification treatment along the direction of normal vector, generate initial candidate viewpoint set and screen out safe viewpoint set.According to the visibility matrix constructed by the sensor model, the optimal viewpoint set is screened out from the safe viewpoint set using the greedy algorithm, and at least one optimal access sequence is solved using a hybrid solution strategy.The scanning path corresponding to each optimal access sequence is constructed and respectively issued to the unmanned aerial vehicle.The technical scheme can improve the detection efficiency and safety of the upper surface of the aircraft, and enhance the degree of automation and detection stability of the system.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The present disclosure relates to the field of bio-information technologies, in particular to a single-device multi-physiological-parameter synchronization and fusion method and a multi-device multi-physiological-parameter synchronization device. The single-device multi-physiological-parameter synchronization and fusion method includes the following steps: constructing a one-to-one wireless transmission system; constructing a one-to-many wired transmission system; initiating a synchronization instruction; checking information; matching information to align first collection data packets of the sensor modules. In the present disclosure, the master collection control module sends synchronization information to multiple sensor modules at the same time, and the master collection control module, based on multiple pieces of timestamp information, maps the collection data packets sent from the multiple sensor modules by time mapping, without requiring multiple sensor modules to be series-connected through a same clock source. In this case, the sensor models are not limited and real-time adjustment can be made to the sampling rate, ensuring high synchronization efficiency between multiple sensor modules.

A method of modeling three-dimensional objects from two-dimensional imagery includes receiving, from an imaging device with a sensor, one or more digital images of an object and image metadata representing a focal length of the sensor, a pixel size of the sensor, and a range of each of the one or more digital images; adding, to the one or more digital images, digital markings representing perpendicular axes of the object; computing at least one sensor model of the imaging device based on the image metadata, the digital markings, and the perpendicular axes of the object; computing object-space coordinates of points corresponding to different features of the object based on the at least one sensor model; and generating a three-dimensional model of the object based on the object-space coordinates of the points.

The application discloses an interference compatibility method for an NGSO-FSS system and an adjacent frequency satellite system, and belongs to the technical field of radio communication frequency compatibility.The method solves the problem of interference between the NGSO-FSS system and the adjacent frequency satellite system, and comprises the following steps: at the satellite layer, designing an NGSO constellation configuration and a sensor model of the adjacent frequency satellite system; at the ground end, determining the position and quantity of gateway stations in the NGSO-FSS system; at the link layer, regarding the gateway stations of the NGSO-FSS system as interference sources, considering the total atmospheric attenuation and the free space transmission loss, obtaining the interference power received by the adjacent frequency satellite system, and further adjusting the transmission power of the corresponding antennas of the gateway stations; based on the gateway stations and the NGSO constellation, evaluating the total interference of all active gateway stations for the adjacent frequency satellite system, calculating the interference reduction amount, adjusting the useless transmission power and the NGSO constellation configuration according to the interference reduction amount, and until the interference power level meets the threshold value of the protection criterion.

This invention relates to the field of shock wave pressure measurement technology and discloses a dynamic compensation method for shock wave pressure sensors based on PINN. The method includes: obtaining step response data of the pressure sensor through shock tube calibration experiments and establishing a dynamic characteristic model of the sensor; constructing a physical information neural network, embedding the sensor dynamic characteristic model as a physical layer into the network structure; designing a composite loss function including data loss terms and physical constraint loss terms, and training the physical information neural network based on simulation data; inputting the distorted pressure signal to be corrected into the trained network, and outputting the corrected shock wave pressure signal. This invention achieves signal correction without requiring precise sensor model parameters and can recover the true pressure waveform that conforms to physical laws under strong noise conditions.

This application proposes a method, apparatus, electronic equipment, and program product for constructing and integrating test models for legged robots. First, a data acquisition terminal and a scene modeling configuration module generate a scene model file to build a standardized test scene. A sensor modeling configuration module then generates a sensor simulation model file coupled with the environment based on this file, simulating the robot's perception function. A dynamics modeling configuration module combines the robot's basic information with scene characteristics to generate a validated dynamics model file, simulating the robot's motion function. An integrated testing module loads the three types of models to complete collaborative simulation testing, achieving collaborative verification of perception and motion functions. Finally, the test process configuration is updated to achieve standardized construction and integrated testing of the test model. By relying on standardized models to optimize the testing process, the standardization and adaptability of legged robot simulation testing are improved, providing reliable support for its testing.

This invention relates to the field of spacecraft mission simulation and intelligent control technology, specifically disclosing a system and method for modeling and simulating spacecraft assembly scenarios. The invention constructs a coupled dynamics simulation module encompassing orbital dynamics, attitude dynamics, and robotic arm dynamics; builds a visual simulation module integrating multiple types of sensor models based on Unreal Engine; and employs the ROS2 communication framework to achieve real-time data interaction with external algorithm platforms. This solves the problems of lack of physical realism, real-time interaction capabilities, and open interfaces in existing space assembly simulation technologies. It achieves high-fidelity dynamic simulation of multi-spacecraft collaborative operations, deformation of large flexible structures, and precise control of robotic arms, enhancing the full-chain simulation support capabilities for space assembly missions from scenario construction and dynamics calculation to control algorithm verification.

The application provides a blade arrival time accurate simulation method based on a blade tip timing principle, comprising the following steps: (1) establishing a blade tip timing sensor model based on sensor circumferential and axial layout information; (2) carrying out static force and modal analysis based on a finite element analysis method, and establishing a rotating blade model; (3) continuously processing blade tip finite element discrete nodes of the rotating blade model; (4) establishing a blade arrival time model based on the sensor and the rotating blade model, judging the position relationship between the sensor and the blade from the axial and circumferential directions, and iteratively solving the blade arrival time. The application considers the influence of the axial position change on the simulation model in the blade movement process by simulating the working process of the blade tip timing test system, the result is not dependent on the finite element grid density, and a more accurate blade arrival time can be obtained.

A method trains a data-based sensor model for determining a change-point timing in at least one sensor signal of a cyclic technical process executed by a technical system. The method includes providing sensor signal time series of a sampled measured value and an associated change-point timing, and determining an evaluation signal time series by defining a regular evaluation time window over the sensor signal time series. The method further includes determining training datasets having the evaluation signal time series and the assigned change-point timing, and training the data-based sensor model with the training datasets. The change-point timing is provided as a classification vector. The classification vector encodes a time point within the evaluation time window by a position of an element of the classification vector and encodes a time point before and / or after the evaluation time window by a further position of a further element of the classification vector.

This invention discloses a hydrocyclone pressure adaptive control method based on a soft-sensor model of slurry flow rate and concentration, comprising the following steps: A. Establishing the soft-sensor model and constructing a training dataset: A pressure transmitter, frequency converter, and current sensor are installed on the outlet pipe of the hydrocyclone feed pump; a current sensor is installed on the motor of the feed pump; and a pressure transmitter is installed on the hydrocyclone feed pipeline. Historical datasets are synchronously collected and correlated within an initial period, and manual timed sampling is also performed. This invention achieves indirect and accurate measurement of key parameters: It creatively utilizes the operating data (speed, current, pressure, liquid level) of existing equipment such as pumps and pipelines, and constructs a high-precision soft-sensor model of flow rate and concentration through advanced machine learning algorithms. This model estimates the slurry flow rate and concentration, which are difficult to measure online using traditional instruments, in real time with low cost and high reliability, breaking through the information bottleneck of hydrocyclone optimization control.

The invention relates to the technical field of urban environment perception and sensor deployment, and discloses a perception blind area modeling and sensor optimal deployment method for an urban complex environment, which comprises the following steps of: obtaining urban environment data of a to-be-deployed area, performing three-dimensional rasterized discretization on an urban space, and performing optimal deployment on the to-be-deployed area; constructing a digital twin environment model containing static obstacle and dynamic obstacle distribution; on the basis, aiming at candidate sensor types and performance parameters thereof, a ray casting method is adopted to judge a sight line shielding relation, and combined with a detection attenuation model of the sensors changing along with distance and angle, comprehensive sensing probabilities of a single sensor or multiple sensors to each three-dimensional grid are calculated, and a sensing blind area probability graph is generated; and further constructing a multi-objective optimization function taking the perception coverage rate, the deployment cost, the perception redundancy and the total energy consumption as optimization indexes, carrying out iterative solution by adopting a genetic algorithm under a preset engineering constraint condition, and finally outputting an optimal deployment scheme containing a sensor model, a three-dimensional space position and attitude parameters.

This invention discloses a multi-sensor fusion-based tunneling guidance control method. The method includes: constructing a motor variable cleaning model; receiving a set of motor sensor data and importing it into the motor variable cleaning model to obtain a final set of motor sensor data; constructing a motor variable pre-control algorithm model; inputting the final set of motor sensor data into the model to obtain a fused motor variable sensor model data set; obtaining temporary tunneling state data for motor variables based on the motor variable control data set and analyzing its validity; constructing a motor variable optimization control model; pre-setting motor control parameters; inputting the final set of motor sensor data and motor control parameters into a dynamic adjustment model for motor variables to output optimal temporary tunneling guidance control data for motor variables; analyzing the set of motor control parameters based on the temporary tunneling guidance data to obtain motor parameter sensitivity; constructing a motor parameter optimization priority model; analyzing and determining the optimization priority of motor control parameters based on motor parameter sensitivity; and inputting the optimized set of motor control parameters into the motor variable optimization control model to obtain the final tunneling motor variable guidance control data.

A method of modeling three-dimensional objects from two-dimensional imagery includes receiving, from an imaging device with a sensor, one or more digital images of an object and image metadata representing a focal length of the sensor, a pixel size of the sensor, and a range of each of the one or more digital images; adding, to the one or more digital images, digital markings representing perpendicular axes of the object; computing at least one sensor model of the imaging device based on the image metadata, the digital markings, and the perpendicular axes of the object; computing object-space coordinates of points corresponding to different features of the object based on the at least one sensor model; and generating a three-dimensional model of the object based on the object-space coordinates of the points.

The application discloses a multi-sensor data space-time consistent simulation method for low-altitude unmanned aerial vehicle monitoring, belongs to the technical field of sensor imaging simulation, and particularly relates to the technical field of space-time consistent multi-sensor imaging synchronous simulation for low-altitude unmanned aerial vehicle monitoring. The method solves the problems of the existing multi-sensor simulation process isolation, data synchronization and low data authenticity, and comprises the following steps: a visible light sensor model, an infrared sensor model and a radar electromagnetic radiation model perform synchronous simulation on a unified simulation object based on the same space-time reference. The multi-sensor data space-time consistent simulation method for low-altitude unmanned aerial vehicle monitoring is suitable for the low-altitude economic field, such as environmental monitoring, emergency rescue, infrastructure inspection, natural resource supervision and low-altitude safety supervision, which needs multi-modal perception and intelligent analysis.